Generally, conventional spectrum analyzers incorporate an architecture that converts a radio frequency (RF) input signal to an intermediate frequency (IF) signal for detection, through a mixing process. Direct RF detection is possible, for example, using a wideband analog-to-digital converter (ADC). However, wideband ADCs have limited dynamic range, tend to have high noise floors, and are relatively expensive.
A common mixing process incorporates a frequency converter, including a local-oscillator (LO) that generates an LO signal and a mixer that mixes the generated LO signal with the RF input signal to convert the RF frequency to a smaller intermediate frequency (IF), at which detection via a conventional ADC can be performed with less noise and at lower cost. The LO is tuned to generate an LO signal having an LO frequency that is higher or lower than the RF frequency of the RF input signal by the value of the IF. When the LO is tuned to an LO frequency higher than the RF frequency, and the intended RF detection band is lower by the IF, signals present at the input of the mixer an IF spacing above the LO frequency will also convert to the IF range and be detected. Such a signal may be referred to as an “image signal,” which is not a “real signal” (or “actual signal”), but rather a signal that does not exist. The presence of an image signal is particularly problematic when trying to detect a signal having a relatively small amplitude at the RF frequency, while a large amplitude signal is present at the frequency of the image signal.
Historically, spectrum analyzers had no practical way of preventing such image signals, and therefore incorporated a method called “signal-ID,” according to which the LO from the above the RF signal was switched to below the RF signal. In this manner, when the detected signal was at the RF frequency, the IF signal would not change. However, when the detected signal was an image signal, the detected signal would shift from the IF frequency to three-times the IF frequency, enabling identification of the detected signal as an image signal.
More recent conventional spectrum analyzers incorporate more complex procedures, using additional circuitry to create substantially image-free down-converters. This may be accomplished by up-converting the RF band, and then filtering the RF plus LO. The up-conversation causes the image signals to be separated far from the RF signals, and the filtering removes the image signals. The filtered, image-free first-converted signal is then down-converted to an IF to be detected. Because of the filtering, there are no unwanted signals present at the input of the second converter to cause image signals. However, this up-conversion technique is difficult and costly for higher frequency spectrum analyzers (e.g., 1 GHz and above).
Therefore, another technique was developed using a swept-tuned pre-selector, such as a YIG-tuned filter, to pre-filter the input RF signal to remove image signals before they arrive at the first converter. Current technology generally limits such pre-selectors to frequencies above about 2 GHz, or even above about 3.6 GHz. Notably, the pre-selector has the added advantage of removing large out-of-band signals that can otherwise cause compression of the input converter. However, the pre-selection technique is expensive, and cannot be used for multi-function instruments (such as PNA-X network analyzers, available from Keysight Technologies, Inc., which includes vector network analysis (VNA) as well as spectrum analysis capabilities) because the filtering, particularly tunable YIG-tuned filters, are not stable or repeatable enough to support stable phase and amplitude response through the filter. Further, the filtering to remove image signals is not exact, so some residual image signals may still be present in the response.
In addition, non-preselected versions of spectrum analyzers have been introduced, which use digital processing to remove image signals. For example, one such non-preselection spectrum analyzer uses a single-conversion mixer, but takes two data acquisitions. One data acquisition is taken with the LO frequency lower than the RF frequency of the input signal, and the other is taken with the LO frequency higher than the RF frequency. The spectrum analyzer processes the two signals, and thereby chooses the minimum of the two signals for display. When a real signal is present, it appears in both data acquisitions, and thus the proper level is displayed. When the signal is an image signal, it is large in one data acquisition and does not appear in the other data acquisition. Thus, the minimum of the two data acquisitions would be essentially the noise floor. The dual data acquisition technique is workable, except when the RF input signal comprises a two-tone or multi-tone signal that exactly matches two-times the IF. In this case, the image of one of the multiple tones will appear in the IF, resulting in erroneous detection. The spectrum analyzer may use a random LO for each acquisition to make this outcome less likely in a two tone case. However, when an unknown multi-tone signal is being measured, the random LO process practically ensures that occasionally the multi-tone signal will land on exactly the offset of the random LO and show a spurious signal. If randomization of the LO is turned off, there is no guarantee that the multi-tone signal will not land exactly on the LO offset and show a continuous image signal.
Thus, what is needed is improved discrimination of image signals, particularly with respect to multi-tone signals (having more than two-tones).